Breed identification using breed-informative SNPs and machine learning based on whole genome sequence data and SNP chip data.

ABSTRACT:

Background

Breed identification is useful in a variety of biological contexts. Breed identification usually involves two stages, i.e., detection of breed-informative SNPs and breed assignment. For both stages, there are several methods proposed. However, what is the optimal combination of these methods remain unclear. In this study, using the whole genome sequence data available for 13 cattle breeds from Run 8 of the 1,000 Bull Genomes Project, we compared the combinations of three methods (Delta, F_ST, and I_n) for breed-informative SNP detection and five machine learning methods (KNN, SVM, RF, NB, and ANN) for breed assignment with respect to different reference population sizes and difference numbers of most breed-informative SNPs. In addition, we evaluated t

SUBMITTER: Zhao C 

PROVIDER: S-EPMC10234014 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature